考虑机器故障的柔性作业车间鲁棒节能调度方法研究

doi:10.16731/j.cnki.1671-3133.2025.10.001

摘要/Abstract

摘要： 在制造业的生产调度领域,不确定性与能耗问题备受关注。机器故障作为影响调度任务和车间能耗的一个关键不确定因素,其随机性对生产过程与能耗优化具有显著的影响。然而,目前关于机器故障情况下的柔性作业车间节能调度方案选择的研究相对较少。为助力节能减排,首先建立了一种鲁棒选择模型,旨在快速选择质量较优且节能的重调度方案。其次,设计了一种改进的多目标果蝇优化算法求解该模型。最后,通过10个公开算例和4个不同的机器故障场景验证了所提算法与模型的有效性和实用性。结果表明,与其他7种算法相比,改进的多目标果蝇优化算法有助于输出最优的调度方案;同时,鲁棒选择模型在辅助选择更优的节能调度方案方面发挥了重要作用,为实现节能减排目标提供了有力支持。

关键词: 柔性作业车间调度, 节能, 机器故障, 鲁棒性, 改进的多目标果蝇优化算法

Abstract: In the field of production scheduling within the manufacturing industry,uncertainty and energy consumption have garnered significant attention. Machine failure,as a key uncertainty factor affecting scheduling tasks and job shop energy consumption,has a substantial impact on production processes and energy optimization. However,there is relatively little research on selecting an energy-efficient scheduling scheme for flexible job shop under machine failure. To promote energy savings and emission reduction,a robust selection model has been developed to swiftly identify a higher-quality,energy-saving rescheduling scheme. Additionally,an improved multi-objective fruit fly optimization algorithm has been designed to solve this model. The effectiveness and practicality of the proposed algorithm and model are validated through 10 benchmark cases and 4 different machine failure scenarios. The results demonstrate that the improved multi-objective fruit fly optimization algorithm outperforms seven other algorithms in generating the optimal scheduling scheme. Meanwhile,the robust selection model plays a crucial role in identifying a better energy-saving scheduling scheme,providing strong support for achieving energy-saving and emission reduction goals.

Key words: flexible job shop scheduling, energy conversation, machine failure, robustness, improved multi-objective fruit fly optimization algorithm

中图分类号:

TP18

李开心, 尹瑞雪, 周鹏, 陈光林. 考虑机器故障的柔性作业车间鲁棒节能调度方法研究[J]. 现代制造工程, 2025, 541(10): 1-15.

LI Kaixin, YIN Ruixue, ZHOU Peng, CHEN Guanglin. Research on robust energy-saving scheduling method for flexible job shop considering machine failure[J]. Modern Manufacturing Engineering, 2025, 541(10): 1-15.

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